OpenRaiser/Intern-Atlas huggingface dataset:
A Temporal Graph Model to Study the Dynamics of Collective Behavior and Performance in Team Sports: An Application to Basketball
Venue: arXiv cs.DM 2024
Year: 2024

In this study, a temporal graph model is designed to model the behavior of collective sports teams based on the networks of player interactions. The main motivation for the model is to integrate the temporal dimension into the analysis of players' passing networks in order to gain deeper insights into the dynamics of system behavior, particularly how a system exploits the degeneracy property to self-regulate. First, the temporal graph model and the entropy measures used to assess the complexity of the dynamics of the network structure are introduced and illustrated. Second, an experiment using basketball data is conducted to investigate the relationship between the complexity level and team performance. This is accomplished by examining the correlations between the entropy measures in a team's behavior and the team's final performance, as well as the link between the relative score compared to that of the opponent and the entropy in the team's behavior. Results indicate positive correlations between entropy measures and final team performance, and threshold values of relative score associated with changes in team behavior -- thereby revealing common and unique team signatures. From a complexity science perspective, the model proves useful for identifying key performance factors in team sports and for studying the effects of given constraints on the exploitation of degeneracy to organize team behavior through various network structures. Future research can easily extend the model and apply it to other types of social networks.
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
Venue: arXiv cs.LG 2024
Year: 2024

We study adaptive combinatorial maximization, which is a core challenge in machine learning, with applications in active learning as well as many other domains. We study the Bayesian setting, and consider the objectives of maximization under a cardinality constraint and minimum cost coverage. We provide new comprehensive approximation guarantees that subsume previous results, as well as considerably strengthen them. Our approximation guarantees simultaneously support the maximal gain ratio as well as near-submodular utility functions, and include both maximization under a cardinality constraint and a minimum cost coverage guarantee. In addition, we provided an approximation guarantee for a modified prior, which is crucial for obtaining active learning guarantees that do not depend on the smallest probability in the prior. Moreover, we discover a new parameter of adaptive selection policies, which we term the "maximal gain ratio". We show that this parameter is strictly less restrictive than the greedy approximation parameter that has been used in previous approximation guarantees, and show that it can be used to provide stronger approximation guarantees than previous results. In particular, we show that the maximal gain ratio is never larger than the greedy approximation factor of a policy, and that it can be considerably smaller. This provides a new insight into the properties that make a policy useful for adaptive combinatorial maximization.
DSGNN: A Dual-View Supergrid-Aware Graph Neural Network for Regional Air Quality Estimation
Venue: arXiv cs.LG 2024
Year: 2024

Air quality estimation can provide air quality for target regions without air quality stations, which is useful for the public. Existing air quality estimation methods divide the study area into disjointed grid regions, and apply 2D convolution to model the spatial dependencies of adjacent grid regions based on the first law of geography, failing to model the spatial dependencies of distant grid regions. To this end, we propose a Dual-view Supergrid-aware Graph Neural Network (DSGNN) for regional air quality estimation, which can model the spatial dependencies of distant grid regions from dual views (i.e., satellite-derived aerosol optical depth (AOD) and meteorology). Specifically, images are utilized to represent the regional data (i.e., AOD data and meteorology data). The dual-view supergrid learning module is introduced to generate supergrids in a parameterized way. Based on the dual-view supergrids, the dual-view implicit correlation encoding module is introduced to learn the correlations between pairwise supergrids. In addition, the dual-view message passing network is introduced to implement the information interaction on the supergrid graphs and images. Extensive experiments on two real-world datasets demonstrate that DSGNN achieves the state-of-the-art performances on the air quality estimation task, outperforming the best baseline by an average of 19.64% in MAE.
Cash or Non-Cash? Unveiling Ideators' Incentive Preferences in Crowdsourcing Contests
Venue: arXiv cs.HC 2024
Year: 2024

Even though research has repeatedly shown that non-cash incentives can be effective, cash incentives are the de facto standard in crowdsourcing contests. In this multi-study research, we quantify ideators' preferences for non-cash incentives and investigate how allowing ideators to self-select their preferred incentive -- offering ideators a choice between cash and non-cash incentives -- affects their creative performance. We further explore whether the market context of the organization hosting the contest -- social (non-profit) or monetary (for-profit) -- moderates incentive preferences and their effectiveness. We find that individuals exhibit heterogeneous incentive preferences and often prefer non-cash incentives, even in for-profit contexts. Offering ideators a choice of incentives can enhance creative performance. Market context moderates the effect of incentives, such that ideators who receive non-cash incentives in for-profit contexts tend to exert less effort. We show that heterogeneity of ideators' preferences (and the ability to satisfy diverse preferences with suitably diverse incentive options) is a critical boundary condition to realizing benefits from offering ideators a choice of incentives. We provide managers with guidance to design effective incentives by improving incentive-preference fit for ideators.
Preuve de concept d'un bot vocal dialoguant en wolof
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents the proof-of-concept of the first automatic voice assistant ever built in Wolof language, the main vehicular language spoken in Senegal. This voicebot is the result of a collaborative research project between Orange Innovation in France, Orange Senegal (aka Sonatel) and ADNCorp, a small IT company based in Dakar, Senegal. The purpose of the voicebot is to provide information to Orange customers about the Sargal loyalty program of Orange Senegal by using the most natural mean to communicate: speech. The voicebot receives in input the customer's oral request that is then processed by a SLU system to reply to the customer's request using audio recordings. The first results of this proof-of-concept are encouraging as we achieved 22\% of WER for the ASR task and 78\% of F1-score on the NLU task.
Transformers as Transducers
Venue: arXiv cs.FL 2024
Year: 2024

We study the sequence-to-sequence mapping capacity of transformers by relating them to finite transducers, and find that they can express surprisingly large classes of transductions. We do so using variants of RASP, a programming language designed to help people "think like transformers," as an intermediate representation. We extend the existing Boolean variant B-RASP to sequence-to-sequence functions and show that it computes exactly the first-order rational functions (such as string rotation). Then, we introduce two new extensions. B-RASP[pos] enables calculations on positions (such as copying the first half of a string) and contains all first-order regular functions. S-RASP adds prefix sum, which enables additional arithmetic operations (such as squaring a string) and contains all first-order polyregular functions. Finally, we show that masked average-hard attention transformers can simulate S-RASP.
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights
Venue: arXiv cs.CL 2024
Year: 2024

This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. Through comprehensive experiments, we demonstrate that incorporating topic sentiment notably enhances the performance of these models. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends. This study contributes to the field by showcasing the potential of NLP in enriching financial analysis and opens up avenues for further research into real-time sentiment analysis and the exploration of emotional and contextual aspects of market sentiment. The integration of advanced NLP techniques like BERTopic with traditional financial analysis methods marks a step forward in developing more sophisticated tools for understanding and predicting market behaviors.
Multitask-based Evaluation of Open-Source LLM on Software Vulnerability
Venue: arXiv cs.SE 2024
Year: 2024

This paper proposes a pipeline for quantitatively evaluating interactive Large Language Models (LLMs) using publicly available datasets. We carry out an extensive technical evaluation of LLMs using Big-Vul covering four different common software vulnerability tasks. This evaluation assesses the multi-tasking capabilities of LLMs based on this dataset. We find that the existing state-of-the-art approaches and pre-trained Language Models (LMs) are generally superior to LLMs in software vulnerability detection. However, in software vulnerability assessment and location, certain LLMs (e.g., CodeLlama and WizardCoder) have demonstrated superior performance compared to pre-trained LMs, and providing more contextual information can enhance the vulnerability assessment capabilities of LLMs. Moreover, LLMs exhibit strong vulnerability description capabilities, but their tendency to produce excessive output significantly weakens their performance compared to pre-trained LMs. Overall, though LLMs perform well in some aspects, they still need improvement in understanding the subtle differences in code vulnerabilities and the ability to describe vulnerabilities to fully realize their potential. Our evaluation pipeline provides valuable insights into the capabilities of LLMs in handling software vulnerabilities.
Red-Teaming Segment Anything Model
Venue: arXiv cs.CV 2024
Year: 2024

Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities' faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation.
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
Venue: arXiv cs.CV 2024
Year: 2024

Fundus image segmentation on unseen domains is challenging, especially for the over-parameterized deep models trained on the small medical datasets. To address this challenge, we propose a method named Adaptive Feature-fusion Neural Network (AFNN) for glaucoma segmentation on unseen domains, which mainly consists of three modules: domain adaptor, feature-fusion network, and self-supervised multi-task learning. Specifically, the domain adaptor helps the pretrained-model fast adapt from other image domains to the medical fundus image domain. Feature-fusion network and self-supervised multi-task learning for the encoder and decoder are introduced to improve the domain generalization ability. In addition, we also design the weighted-dice-loss to improve model performance on complex optic-cup segmentation tasks. Our proposed method achieves a competitive performance over existing fundus segmentation methods on four public glaucoma datasets.
Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity
Venue: arXiv cs.CL 2024
Year: 2024

Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking. Abstract Meaning Representation (AMR) represents text as knowledge graphs. Evaluating the quality of these graphs involves matching them structurally to each other and semantically to the source text. Existing AMR metrics are inefficient and struggle to capture semantic similarity. We also lack a systematic evaluation benchmark for assessing structural similarity between AMR graphs. To overcome these limitations, we introduce a novel AMR similarity metric, rematch, alongside a new evaluation for structural similarity called RARE. Among state-of-the-art metrics, rematch ranks second in structural similarity; and first in semantic similarity by 1--5 percentage points on the STS-B and SICK-R benchmarks. Rematch is also five times faster than the next most efficient metric.
Segment Any 3D Object with Language
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we investigate Open-Vocabulary 3D Instance Segmentation (OV-3DIS) with free-form language instructions. Earlier works that rely on only annotated base categories for training suffer from limited generalization to unseen novel categories. Recent works mitigate poor generalizability to novel categories by generating class-agnostic masks or projecting generalized masks from 2D to 3D, but disregard semantic or geometry information, leading to sub-optimal performance. Instead, generating generalizable but semantic-related masks directly from 3D point clouds would result in superior outcomes. In this paper, we introduce Segment any 3D Object with LanguagE (SOLE), which is a semantic and geometric-aware visual-language learning framework with strong generalizability by generating semantic-related masks directly from 3D point clouds. Specifically, we propose a multimodal fusion network to incorporate multimodal semantics in both backbone and decoder. In addition, to align the 3D segmentation model with various language instructions and enhance the mask quality, we introduce three types of multimodal associations as supervision. Our SOLE outperforms previous methods by a large margin on ScanNetv2, ScanNet200, and Replica benchmarks, and the results are even close to the fully-supervised counterpart despite the absence of class annotations in the training. Furthermore, extensive qualitative results demonstrate the versatility of our SOLE to language instructions.
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
Venue: arXiv cs.SE 2024
Year: 2024

Recent advancements in automatic code generation using large language model (LLM) agent have brought us closer to the future of automated software development. However, existing single-agent approaches face limitations in generating and improving large-scale, complex codebases due to constraints in context length. To tackle this challenge, we propose Self-Organized multi-Agent framework (SoA), a novel multi-agent framework that enables the scalable and efficient generation and optimization of large-scale code. In SoA, self-organized agents operate independently to generate and modify code components while seamlessly collaborating to construct the overall codebase. A key feature of our framework is the automatic multiplication of agents based on problem complexity, allowing for dynamic scalability. This enables the overall code volume to be increased indefinitely according to the number of agents, while the amount of code managed by each agent remains constant. We evaluate SoA on the HumanEval benchmark and demonstrate that, compared to a single-agent system, each agent in SoA handles significantly less code, yet the overall generated code is substantially greater. Moreover, SoA surpasses the powerful single-agent baseline by 5% in terms of Pass@1 accuracy.
What is to be gained by ensemble models in analysis of spectroscopic data?
Venue: arXiv cs.LG 2024
Year: 2024

An empirical study was carried out to compare different implementations of ensemble models aimed at improving prediction in spectroscopic data. A wide range of candidate models were fitted to benchmark datasets from regression and classification settings. A statistical analysis using linear mixed model was carried out on prediction performance criteria resulting from model fits over random splits of the data. The results showed that the ensemble classifiers were able to consistently outperform candidate models in our application
A Generative Deep Learning Approach for Crash Severity Modeling with Imbalanced Data
Venue: arXiv cs.LG 2024
Year: 2024

Crash data is often greatly imbalanced, with the majority of crashes being non-fatal crashes, and only a small number being fatal crashes due to their rarity. Such data imbalance issue poses a challenge for crash severity modeling since it struggles to fit and interpret fatal crash outcomes with very limited samples. Usually, such data imbalance issues are addressed by data resampling methods, such as under-sampling and over-sampling techniques. However, most traditional and deep learning-based data resampling methods, such as synthetic minority oversampling technique (SMOTE) and generative Adversarial Networks (GAN) are designed dedicated to processing continuous variables. Though some resampling methods have improved to handle both continuous and discrete variables, they may have difficulties in dealing with the collapse issue associated with sparse discrete risk factors. Moreover, there is a lack of comprehensive studies that compare the performance of various resampling methods in crash severity modeling. To address the aforementioned issues, the current study proposes a crash data generation method based on the Conditional Tabular GAN. After data balancing, a crash severity model is employed to estimate the performance of classification and interpretation. A comparative study is conducted to assess classification accuracy and distribution consistency of the proposed generation method using a 4-year imbalanced crash dataset collected in Washington State, U.S. Additionally, Monte Carlo simulation is employed to estimate the performance of parameter and probability estimation in both two- and three-class imbalance scenarios. The results indicate that using synthetic data generated by CTGAN-RU for crash severity modeling outperforms using original data or synthetic data generated by other resampling methods.
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
Venue: arXiv cs.CL 2024
Year: 2024

Low-resource languages face significant barriers in AI development due to limited linguistic resources and expertise for data labeling, rendering them rare and costly. The scarcity of data and the absence of preexisting tools exacerbate these challenges, especially since these languages may not be adequately represented in various NLP datasets. To address this gap, we propose leveraging the potential of LLMs in the active learning loop for data annotation. Initially, we conduct evaluations to assess inter-annotator agreement and consistency, facilitating the selection of a suitable LLM annotator. The chosen annotator is then integrated into a training loop for a classifier using an active learning paradigm, minimizing the amount of queried data required. Empirical evaluations, notably employing GPT-4-Turbo, demonstrate near-state-of-the-art performance with significantly reduced data requirements, as indicated by estimated potential cost savings of at least 42.45 times compared to human annotation. Our proposed solution shows promising potential to substantially reduce both the monetary and computational costs associated with automation in low-resource settings. By bridging the gap between low-resource languages and AI, this approach fosters broader inclusion and shows the potential to enable automation across diverse linguistic landscapes.
Smooth Deep Saliency
Venue: arXiv cs.CV 2024
Year: 2024

In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling. Those methods make the investigated models more interpretable for gradient-based saliency maps, computed in hidden layers. We evaluate the faithfulness of those methods using insertion and deletion metrics, finding that saliency maps computed in hidden layers perform better compared to both the input layer and GradCAM. We test our approach on different models trained for image classification on ImageNet1K, and models trained for tumor detection on Camelyon16 and in-house real-world digital pathology scans of stained tissue samples. Our results show that the checkerboard noise in the gradient gets reduced, resulting in smoother and therefore easier to interpret saliency maps.
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load monitoring requires sensors inside the bearing itself. Recently introduced sensor rollers enable direct bearing load monitoring but are constrained by their battery life. Data-driven virtual sensors can learn from sensor roller data collected during a batterys lifetime to map operating conditions to bearing loads. Although spatially distributed bearing sensors offer insights into load distribution (e.g., correlating temperature with load), traditional machine learning algorithms struggle to fully exploit these spatial-temporal dependencies. To address this gap, we introduce a graph-based virtual sensor that leverages Graph Neural Networks (GNNs) to analyze spatial-temporal dependencies among sensor signals, mapping existing measurements (temperature, vibration) to bearing loads. Since temperature and vibration signals exhibit vastly different dynamics, we propose Heterogeneous Temporal Graph Neural Networks (HTGNN), which explicitly models these signal types and their interactions for effective load prediction. Our results demonstrate that HTGNN outperforms Convolutional Neural Networks (CNNs), which struggle to capture both spatial and heterogeneous signal characteristics. These findings highlight the importance of capturing the complex spatial interactions between temperature, vibration, and load.